Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Accurate identification and classification of a large number of components using a typical 2D camera under insufficient lighting is one of the challenges in image recognition. For example, object identification may involve detection of the edges of an object in an image. Without sufficient lighting, the edges may be undetectable. Thus, satisfactory image recognition may not be achieved without sufficient lighting. Detection of an orientation of the object may present another challenge. Typically, the orientation of a rotating object in an image may be unknown at the time of image acquisition. The lack of orientation information may increase a possibility of misidentification of the object. Image recognition of a rotating object may be achieved by recognizing an object in rotated and unrotated states as being the same object. That is, images of the object in rotated and unrotated states need both to be learned to achieve correct recognition of the object in a target image. Conventional approaches of creating a 3D object from 2D images involve an operator assigning individual 3D coordinates to the image.